大規模言語モデルはロボット有限状態機械の修正を支援できるか
Can Large Language Models Help Developers with Robotic Finite State Machine Modification?
大規模言語モデルを用いて、自然言語指示からロボットの有限状態機械（FSM）コードを編集・生成する手法を提案し、実世界のロボットデータセットで有効性を検証した。
著者: Xiangyu Robin Gan, Yuxin Ray Song, Nick Walker, Maya Cakmak
分類: cs.RO
原文アブストラクト
Finite state machines (FSMs) are widely used to manage robot behavior logic, particularly in real-world applications that require a high degree of reliability and structure. However, traditional manual FSM design and modification processes can be time-consuming and error-prone. We propose that large language models (LLMs) can assist developers in editing FSM code for real-world robotic use cases. LLMs, with their ability to use context and process natural language, offer a solution for FSM modification with high correctness, allowing developers to update complex control logic through natural language instructions. Our approach leverages few-shot prompting and language-guided code generation to reduce the amount of time it takes to edit an FSM. To validate this approach, we evaluate it on a real-world robotics dataset, demonstrating its effectiveness in practical scenarios.
